如何在 CUDA 中使用多态性 [英] How to use polymorphism in CUDA
问题描述
我正在将一些物理模拟代码从 C++ 移植到 CUDA.
I am porting some physics simulation code from C++ to CUDA.
基本算法可以理解为:对向量的每个元素应用一个算子.在伪代码中,模拟可能包括以下内核调用:
The fundamental algorithm can be understood as: applying an operator to each element of a vector. In pseudocode, a simulation might include the following kernel call:
apply(Operator o, Vector v){
...
}
例如:
apply(add_three_operator, some_vector)
将为向量中的每个元素添加三个.
would add three to each element in the vector.
在我的 C++ 代码中,我有一个抽象基类 Operator,具有许多不同的具体实现.重要的方法是类运算符{虚拟双操作(双 x)=0;运算符 compose(运算符 lo,运算符 ro);...}
In my C++ code, I have an abstract base class Operator, with many different concrete implementations. The important method is class Operator{ virtual double operate(double x) =0; Operator compose(Operator lo, Operator ro); ... }
AddOperator 的实现可能如下所示:
The implementation for AddOperator might look like this:
class AddOperator : public Operator{
private:
double to_add;
public:
AddOperator(double to_add): to_add(to_add){}
double operator(double x){
return x + to_add;
}
};
运算符类具有缩放和组合运算符具体实现的方法.这种抽象让我可以简单地将叶"运算符组合成更一般的转换.
The operator class has methods for scaling and composing concrete implementations of Operator. This abstraction allows me to simply compose "leaf" operators into more general transformations.
例如:
apply(compose(add_three_operator, square_operator), some_vector);
将添加三个然后平方向量的每个元素.
would add three then square each element of the vector.
问题是 CUDA 不支持内核中的虚拟方法调用.我目前的想法是使用模板.然后内核调用看起来像:
The problem is CUDA doesn't support virtual method calls in the kernel. My current thought is to use templates. Then kernel calls will look something like:
apply<Composition<AddOperator,SquareOperator>>
(compose(add_three_operator, square_operator), some_vector);
有什么建议吗?
推荐答案
可能是这样的......
Something like this perhaps...
template <class Op1, class Op2>
class Composition {...}
template <class Op1, class Op2>
Composition<Op1, Op2> compose(Op1& op1, Op2& op2) {...}
template<class C>
void apply(C& c, VecType& vec){...}
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